Topic: CNN (Convolutional Neural Networks) – Part 4: Training, Loss Functions, and Evaluation Metrics

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1. Preparing for Training

To train a CNN, we need:

• Dataset – Typically image data with labels (e.g., MNIST, CIFAR-10).

• Loss Function – Measures the difference between predicted and actual values.

• Optimizer – Updates model weights based on gradients.

• Evaluation Metrics – Accuracy, precision, recall, F1 score, etc.

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2. Common Loss Functions for CNNs

• CrossEntropyLoss – For multi-class classification (most common).

criterion = nn.CrossEntropyLoss()

• BCELoss – For binary classification.

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3. Optimizers

• SGD (Stochastic Gradient Descent)
• Adam – Adaptive learning rate; widely used for faster convergence.

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

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4. Basic Training Loop in PyTorch

for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0

    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()

    print(f"Epoch {epoch+1}, Loss: {running_loss:.4f}")

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5. Evaluating the Model

correct = 0
total = 0
model.eval()

with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f"Test Accuracy: {accuracy:.2f}%")

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6. Tips for Better CNN Training

• Normalize images.

• Shuffle training data for better generalization.

• Use validation sets to monitor overfitting.

• Save checkpoints (torch.save(model.state_dict())).

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Summary

• CNN training involves feeding batches of images, computing loss, backpropagation, and updating weights.

• Evaluation metrics like accuracy help track progress.

• Loss functions and optimizers are critical for learning quality.

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Exercise

• Train a CNN on CIFAR-10 for 10 epochs using CrossEntropyLoss and Adam, then print accuracy and plot loss over epochs.

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#CNN #DeepLearning #Training #LossFunction #ModelEvaluation

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🤖🧠 DeepEval: The Ultimate LLM Evaluation Framework for AI Developers

🗓️ 07 Oct 2025
📚 AI News & Trends

In today’s AI-driven world, large language models (LLMs) have become central to modern applications from chatbots to intelligent AI agents. However, ensuring the accuracy, reliability and safety of these models is a significant challenge. Even small errors, biases or hallucinations can result in misleading information, frustrated users or business setbacks. This is where DeepEval, an ...

#DeepEval #LLM #AIDevelopment #LanguageModels #ModelEvaluation #ArtificialIntelligence